Gastos Casa
03 February, 2025
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 27/11/2024 | Electricidad | 45000 | Andrés | Eléctrico |
| 30/11/2024 | Comida | 62899 | Tami | Supermercado |
| 11/12/2024 | Comida | 17738 | Andrés | compra lider despues viaje |
| 12/12/2024 | Comida | 55616 | Tami | Supermercado |
| 14/12/2024 | Diosi | 16276 | Andrés | antiparasitario |
| 15/12/2024 | Comida | 23773 | Tami | Supermercado |
| 18/12/2024 | Comida | 41554 | Tami | Supermercado |
| 18/12/2024 | VTR | 22000 | Andrés | NA |
| 20/12/2024 | Plata basureros | 10000 | Tami | NA |
| 3/12/2024 | Agua | 15828 | Andrés | PAC AGUAS ANDIN 000000005687837 |
| 22/12/2024 | Comida | 46608 | Tami | Supermercado |
| 10/12/2024 | Otros | 47484 | Andrés | viaje brasil |
| 29/12/2024 | Electricidad | 27417 | Andrés | NA |
| 29/12/2024 | Comida | 83284 | Tami | Supermercado |
| 30/12/2024 | Comida | 30000 | Andrés | nueces almendras mix etc |
| 30/12/2024 | Otros | 47484 | Andrés | viaje a brasil (duplicar para q cargue sobre tami) |
| 4/1/2025 | Diosi | 53999 | Andrés | n y d pumpkin 7.5 |
| 4/1/2025 | Comida | 15260 | Andrés | NA |
| 6/1/2025 | Comida | 40988 | Tami | Supermercado |
| 8/1/2025 | Pago cámaras MB | 20000 | Tami | NA |
| 13/1/2025 | Comida | 67387 | Tami | Supermercado |
| 20/1/2025 | Comida | 21692 | Andrés | gnoccis |
| 20/1/2025 | Comida | 86884 | Tami | Supermercado |
| 21/1/2025 | Comida | 21525 | Andrés | piwen |
| 23/1/2025 | VTR | 21990 | Andrés | NA |
| 25/1/2025 | Diosi | 20000 | Andrés | arena diosi |
| 27/1/2025 | Comida | 71516 | Tami | Supermercado |
| 30/1/2025 | Electricidad | 55000 | Andrés | NA |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 9.9853e+08 2 5.0156 0.0068 **
## lag_depvar 2.6100e+11 1 2621.9481 <2e-16 ***
## Residuals 8.0232e+10 806
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 -1946.28 16403.96 0.1542039
## 2-0 31597.659 23332.04 39863.28 0.0000000
## 2-1 24368.821 19573.89 29163.75 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 633 77359.43 2 88593.71
## 634 79878.71 2 77359.43
## 635 81753.00 2 79878.71
## 636 75716.00 2 81753.00
## 637 67381.43 2 75716.00
## 638 63528.57 2 67381.43
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## 649 82700.86 2 81666.29
## 650 89422.14 2 82700.86
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## 652 98812.71 2 104812.71
## 653 64779.86 2 98812.71
## 654 61862.86 2 64779.86
## 655 58376.43 2 61862.86
## 656 59503.57 2 58376.43
## 657 55429.43 2 59503.57
## 658 44454.57 2 55429.43
## 659 47184.00 2 44454.57
## 660 52126.71 2 47184.00
## 661 51202.00 2 52126.71
## 662 64437.14 2 51202.00
## 663 64297.14 2 64437.14
## 664 64628.57 2 64297.14
## 665 51413.14 2 64628.57
## 666 52969.43 2 51413.14
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## 669 41907.86 2 48799.43
## 670 45382.00 2 41907.86
## 671 42633.29 2 45382.00
## 672 46624.71 2 42633.29
## 673 44051.86 2 46624.71
## 674 35852.86 2 44051.86
## 675 29737.71 2 35852.86
## 676 29734.86 2 29737.71
## 677 32881.71 2 29734.86
## 678 38298.57 2 32881.71
## 679 40886.14 2 38298.57
## 680 38601.86 2 40886.14
## 681 38628.86 2 38601.86
## 682 39142.57 2 38628.86
## 683 32666.14 2 39142.57
## 684 39911.57 2 32666.14
## 685 39336.29 2 39911.57
## 686 39678.86 2 39336.29
## 687 41963.14 2 39678.86
## 688 54220.57 2 41963.14
## 689 63901.86 2 54220.57
## 690 73116.00 2 63901.86
## 691 60863.86 2 73116.00
## 692 56293.86 2 60863.86
## 693 52725.00 2 56293.86
## 694 58625.00 2 52725.00
## 695 47513.00 2 58625.00
## 696 40300.14 2 47513.00
## 697 33312.43 2 40300.14
## 698 29556.71 2 33312.43
## 699 27816.71 2 29556.71
## 700 34120.29 2 27816.71
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## 703 39694.14 2 32902.57
## 704 72501.29 2 39694.14
## 705 79551.14 2 72501.29
## 706 99637.71 2 79551.14
## 707 95424.29 2 99637.71
## 708 98395.14 2 95424.29
## 709 115594.71 2 98395.14
## 710 114267.57 2 115594.71
## 711 88353.29 2 114267.57
## 712 88750.86 2 88353.29
## 713 78835.71 2 88750.86
## 714 75519.14 2 78835.71
## 715 73202.86 2 75519.14
## 716 53433.29 2 73202.86
## 717 48165.71 2 53433.29
## 718 52163.14 2 48165.71
## 719 49306.86 2 52163.14
## 720 36846.86 2 49306.86
## 721 43220.57 2 36846.86
## 722 38952.29 2 43220.57
## 723 41522.29 2 38952.29
## 724 39090.00 2 41522.29
## 725 28452.57 2 39090.00
## 726 32975.00 2 28452.57
## 727 33690.71 2 32975.00
## 728 26405.29 2 33690.71
## 729 47087.43 2 26405.29
## 730 49660.29 2 47087.43
## 731 47409.71 2 49660.29
## 732 53881.71 2 47409.71
## 733 45189.57 2 53881.71
## 734 45503.86 2 45189.57
## 735 54640.14 2 45503.86
## 736 39131.29 2 54640.14
## 737 35024.14 2 39131.29
## 738 44755.43 2 35024.14
## 739 41063.29 2 44755.43
## 740 42783.29 2 41063.29
## 741 45952.57 2 42783.29
## 742 44937.43 2 45952.57
## 743 40838.43 2 44937.43
## 744 48838.43 2 40838.43
## 745 43139.14 2 48838.43
## 746 67134.29 2 43139.14
## 747 73224.29 2 67134.29
## 748 68770.71 2 73224.29
## 749 59539.29 2 68770.71
## 750 82179.86 2 59539.29
## 751 74252.14 2 82179.86
## 752 73015.00 2 74252.14
## 753 56116.43 2 73015.00
## 754 111885.00 2 56116.43
## 755 131425.14 2 111885.00
## 756 136678.00 2 131425.14
## 757 115531.29 2 136678.00
## 758 118310.86 2 115531.29
## 759 117449.43 2 118310.86
## 760 115193.57 2 117449.43
## 761 61025.43 2 115193.57
## 762 43913.86 2 61025.43
## 763 46099.29 2 43913.86
## 764 44524.86 2 46099.29
## 765 42208.71 2 44524.86
## 766 166486.57 2 42208.71
## 767 171565.29 2 166486.57
## 768 200415.71 2 171565.29
## 769 204498.14 2 200415.71
## 770 197558.86 2 204498.14
## 771 195266.57 2 197558.86
## 772 203144.29 2 195266.57
## 773 85493.71 2 203144.29
## 774 74721.57 2 85493.71
## 775 36232.14 2 74721.57
## 776 40161.71 2 36232.14
## 777 40629.86 2 40161.71
## 778 45663.71 2 40629.86
## 779 39252.29 2 45663.71
## 780 39618.57 2 39252.29
## 781 39438.43 2 39618.57
## 782 44650.71 2 39438.43
## 783 38626.71 2 44650.71
## 784 38280.43 2 38626.71
## 785 44134.14 2 38280.43
## 786 47596.43 2 44134.14
## 787 45598.43 2 47596.43
## 788 42564.29 2 45598.43
## 789 45699.14 2 42564.29
## 790 49553.86 2 45699.14
## 791 50018.43 2 49553.86
## 792 43772.86 2 50018.43
## 793 39235.43 2 43772.86
## 794 39905.00 2 39235.43
## 795 40374.43 2 39905.00
## 796 34230.57 2 40374.43
## 797 34324.14 2 34230.57
## 798 33491.57 2 34324.14
## 799 33366.43 2 33491.57
## 800 46646.86 2 33366.43
## 801 49770.86 2 46646.86
## 802 57339.86 2 49770.86
## 803 59799.14 2 57339.86
## 804 53577.14 2 59799.14
## 805 61775.29 2 53577.14
## 806 70627.86 2 61775.29
## 807 57888.43 2 70627.86
## 808 49960.71 2 57888.43
## 809 42923.71 2 49960.71
## 810 47284.86 2 42923.71
## 811 52284.86 2 47284.86
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 654 53831.92 22473.013
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00 56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57 61025.43 43913.86 46099.29 44524.86
## [764] 42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29 85493.71 74721.57 36232.14 40161.71 40629.86 45663.71
## [778] 39252.29 39618.57 39438.43 44650.71 38626.71 38280.43 44134.14
## [785] 47596.43 45598.43 42564.29 45699.14 49553.86 50018.43 43772.86
## [792] 39235.43 39905.00 40374.43 34230.57 34324.14 33491.57 33366.43
## [799] 46646.86 49770.86 57339.86 59799.14 53577.14 61775.29 70627.86
## [806] 57888.43 49960.71 42923.71 47284.86 52284.86
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6
## 2022.959291 4042.117333 -539.769451 2436.599916 -2972.988622
## 7 8 9 10 11
## 517.525915 -5657.642301 -1185.672118 -3963.776281 -413.382062
## 12 13 14 15 16
## -4935.771955 -1602.715405 -893.084563 383.616149 -3238.115889
## 17 18 19 20 21
## -371.715450 -2124.754069 6610.027193 -1529.388791 -1207.741630
## 22 23 24 25 26
## 1476.588879 -1187.232136 234.580674 1694.400779 -7104.172783
## 27 28 29 30 31
## 950.639969 8194.153774 413.533864 -18.618003 -2404.858788
## 32 33 34 35 36
## 1573.971774 4569.157069 1120.419228 2384.723469 -1875.838115
## 37 38 39 40 41
## 4602.223134 4300.393010 -2281.496070 -2984.823908 -1110.926042
## 42 43 44 45 46
## -10741.334729 7297.301903 2558.802281 1366.287953 8103.649593
## 47 48 49 50 51
## 678.870432 6521.886334 6703.599335 -5896.688513 -4803.659976
## 52 53 54 55 56
## -5063.390042 -7928.052565 6136.075646 -4075.671667 -4890.541160
## 57 58 59 60 61
## 3862.672538 890.946541 -30.095062 143.971079 -4995.052460
## 62 63 64 65 66
## 18131.624326 3631.541206 -3656.747567 5918.782603 7333.999028
## 67 68 69 70 71
## 14624.811569 1670.288556 -13233.845708 -1314.698610 4637.110442
## 72 73 74 75 76
## -4909.223872 -4408.613927 -10497.613209 2474.812964 -5394.454180
## 77 78 79 80 81
## 1072.658695 -6859.160798 558.492778 -2344.752289 -2683.360547
## 82 83 84 85 86
## -3920.507312 -524.437456 2326.565094 3771.379127 481.568180
## 87 88 89 90 91
## -480.901905 200.109655 4304.788170 -1163.890466 1150.942870
## 92 93 94 95 96
## -2065.308536 -1043.455047 179.102897 275.951985 -7483.252553
## 97 98 99 100 101
## 2398.326232 -8598.562983 -2930.219688 -4027.856645 -1723.121282
## 102 103 104 105 106
## -1247.803870 3194.009880 -2333.061348 2603.799867 -1151.877441
## 107 108 109 110 111
## 977.114564 2592.158761 -3152.008784 -4718.453315 -842.439904
## 112 113 114 115 116
## 1911.098379 11698.699074 -1247.358016 2665.358523 4257.995316
## 117 118 119 120 121
## 3495.134070 -1109.227854 -4723.494952 -3726.536598 2320.679531
## 122 123 124 125 126
## -1733.605751 1341.043991 8857.805547 839.789867 123.463550
## 127 128 129 130 131
## -2527.399694 2651.772565 7047.615432 1002.651320 -8508.697208
## 132 133 134 135 136
## 1747.834769 4132.974165 -3169.405556 -1421.960918 -854.761466
## 137 138 139 140 141
## -3879.970986 1186.202168 -493.606988 -2911.535119 1722.321800
## 142 143 144 145 146
## -1878.776248 -7825.770370 2048.784145 -3473.169331 2110.699045
## 147 148 149 150 151
## -251.779170 1028.167368 -355.667430 1355.613332 1188.504499
## 152 153 154 155 156
## 3357.231971 -4863.917182 -1172.479569 -3233.142377 5961.618136
## 157 158 159 160 161
## 9746.169559 -3671.080407 -5015.782371 3369.229279 -44.516885
## 162 163 164 165 166
## 2455.546242 -6154.657312 -6987.257379 3921.497601 17151.456861
## 167 168 169 170 171
## 3362.418327 -667.735644 -2713.684479 -1368.083306 3328.264552
## 172 173 174 175 176
## -494.626542 -8341.007126 2608.065640 4065.534127 359.946185
## 177 178 179 180 181
## 8484.284376 -9525.199449 -3735.634253 -11004.335560 -11487.360602
## 182 183 184 185 186
## 999.220644 9052.559863 -1685.474761 5673.433993 6290.119727
## 187 188 189 190 191
## 12881.977077 8133.775042 -4373.135221 2160.997897 10060.908919
## 192 193 194 195 196
## -1966.977015 -2763.497427 -10593.928279 -6659.017388 948.271102
## 197 198 199 200 201
## -5520.551345 -10074.663189 5120.281040 -3341.136048 -1980.885641
## 202 203 204 205 206
## -1070.856896 6227.525374 9601.261466 277.628712 2622.885727
## 207 208 209 210 211
## 2791.488181 5473.612243 12514.564643 -6026.050718 -11619.878458
## 212 213 214 215 216
## -5966.527539 -10875.738877 -5344.105557 1266.021057 -13275.429637
## 217 218 219 220 221
## 16146.072984 7532.970367 1236.039728 26393.225321 12180.858930
## 222 223 224 225 226
## 6970.223268 13654.651108 -4304.297074 -2114.756222 3415.267587
## 227 228 229 230 231
## -1.033193 2393.058024 8655.116080 5474.274419 -2261.450831
## 232 233 234 235 236
## -2170.260313 9094.254597 -11850.573221 -7598.757587 -8840.231772
## 237 238 239 240 241
## -10383.888581 2812.922562 1080.399850 -8571.995921 -9250.172234
## 242 243 244 245 246
## 8848.221748 -8031.979989 2233.141047 -10562.875333 -4299.137059
## 247 248 249 250 251
## 1180.799247 753.813701 -12571.092638 3407.588401 1814.160171
## 252 253 254 255 256
## 3954.867310 1865.737668 -1436.610429 10863.118833 20579.568509
## 257 258 259 260 261
## 2850.208429 -4622.055805 3771.381599 -2038.324915 3400.728234
## 262 263 264 265 266
## -5192.816241 -11220.778932 -5030.443050 -813.443779 -5479.811073
## 267 268 269 270 271
## 8496.296388 -4584.411501 3893.719439 -2413.859912 4127.767738
## 272 273 274 275 276
## 394.508768 6986.431589 -1746.174165 11695.212223 -4943.486105
## 277 278 279 280 281
## 1379.253601 -720.881261 7505.979075 -5420.468129 -3077.050565
## 282 283 284 285 286
## -11596.606374 -2971.981168 18359.294105 7437.329169 2369.479841
## 287 288 289 290 291
## -996.598367 544.116247 6037.818540 6508.314534 -19160.213638
## 292 293 294 295 296
## -11464.944087 -8411.117834 9400.001099 2777.531682 -1482.377506
## 297 298 299 300 301
## 27102.854956 9684.044182 4495.958648 9107.517889 2427.511399
## 302 303 304 305 306
## -1457.947975 7486.196599 -24718.697657 -3868.073864 -491.377044
## 307 308 309 310 311
## -7279.346793 -4256.651599 2662.038031 -9471.257276 -3477.564051
## 312 313 314 315 316
## -8423.727373 1353.414382 -3372.725568 1832.895250 -4310.365823
## 317 318 319 320 321
## 27226.004186 -1061.962930 2957.983579 10488.199871 5213.511553
## 322 323 324 325 326
## 31993.081593 4624.241239 -21419.510189 1402.652067 725.791688
## 327 328 329 330 331
## -6842.737019 -2079.250509 -33599.226283 709.140214 -2479.633933
## 332 333 334 335 336
## -263.966068 -3341.212955 3920.446020 -622.097114 -7139.259632
## 337 338 339 340 341
## -3280.496567 -2348.985431 -7834.373191 3719.494365 -1527.173457
## 342 343 344 345 346
## -1895.509038 -1151.692901 15.891326 313.557818 -1794.883985
## 347 348 349 350 351
## -9622.820440 -13356.516913 2205.150000 -4449.731593 -3778.669867
## 352 353 354 355 356
## -6096.806176 1645.732661 1258.989122 2609.942327 -3932.133063
## 357 358 359 360 361
## -676.257602 509.951142 6834.655925 60.866994 -259.520840
## 362 363 364 365 366
## 2358.286854 -2988.679391 -1104.952171 -8968.263020 -4813.669647
## 367 368 369 370 371
## -6383.521628 -5098.774539 -7387.660560 4902.120614 223.496258
## 372 373 374 375 376
## 6960.805540 -7835.167000 -2432.441391 -3553.376026 -2623.785668
## 377 378 379 380 381
## -12610.287793 1796.696532 -10761.066548 5604.732944 9204.397167
## 382 383 384 385 386
## 2940.504728 -2605.790235 1402.910014 6529.403965 11160.356483
## 387 388 389 390 391
## -6107.928548 -5643.644413 -415.778094 8303.703387 1514.389373
## 392 393 394 395 396
## 10913.953224 -10236.585122 2464.583037 392.237372 241.801477
## 397 398 399 400 401
## -973.580450 -876.370587 -14794.729929 8291.208230 -1449.100697
## 402 403 404 405 406
## -1631.742264 6731.278903 -8214.898935 -1536.924620 -2762.815116
## 407 408 409 410 411
## -6036.036613 -3046.610308 -4092.330485 -8914.709428 6010.876456
## 412 413 414 415 416
## 1478.523077 -7550.371462 -7839.071352 14103.367842 3615.059867
## 417 418 419 420 421
## 4263.733608 -8291.464233 -4960.452476 -2794.994787 2636.356218
## 422 423 424 425 426
## -14212.053977 -2927.043188 -9228.347190 2919.011364 6855.636267
## 427 428 429 430 431
## 6407.868577 -4195.546154 -4312.758671 -4897.866443 -1947.290688
## 432 433 434 435 436
## -5867.485336 -6761.706087 -6061.798345 -1488.168815 -949.045917
## 437 438 439 440 441
## -5084.975004 2483.565026 4715.444020 -5215.538738 -2300.489467
## 442 443 444 445 446
## 1435.588637 -3994.518794 2689.144895 -6746.542885 -12253.762368
## 447 448 449 450 451
## -4605.898346 9559.348884 -2176.087030 4612.194030 -6041.652628
## 452 453 454 455 456
## -1272.138254 232.916564 2866.988611 -12447.646259 3241.967074
## 457 458 459 460 461
## -6852.715307 6394.664742 2846.010016 2321.640499 -4046.145656
## 462 463 464 465 466
## 1908.053751 -204.897711 1593.320640 -731.012987 3142.004301
## 467 468 469 470 471
## -2863.953575 5593.630310 -7179.779975 -3166.401984 -2392.955035
## 472 473 474 475 476
## -4841.788504 2838.022890 7621.262734 -6231.280235 1298.495464
## 477 478 479 480 481
## -6372.554880 -3010.682983 1855.459024 -13099.550180 -9871.417821
## 482 483 484 485 486
## -1285.025123 -69.516389 -1063.803000 -1449.898556 -9696.951168
## 487 488 489 490 491
## 11014.674664 6093.550121 7244.420851 -5648.576064 5179.331425
## 492 493 494 495 496
## 9079.320101 5799.516746 -13750.307089 -10776.101757 -3603.341107
## 497 498 499 500 501
## -1255.880942 -673.766948 -7777.471911 488.885047 4156.139092
## 502 503 504 505 506
## 5352.380472 476.190775 -107.784076 -7428.482863 411.349863
## 507 508 509 510 511
## -5212.708029 1687.112808 -1454.450149 -8313.872858 -727.051573
## 512 513 514 515 516
## -2803.813369 -712.231155 1203.066686 -9636.781880 -7872.241193
## 517 518 519 520 521
## 24202.911496 9626.137909 5637.275080 -5599.729306 2562.757457
## 522 523 524 525 526
## 16774.342191 11158.867195 -24503.115489 -5295.307802 -3943.344705
## 527 528 529 530 531
## 4379.374117 -570.115406 -11313.558357 4229.466125 13725.099754
## 532 533 534 535 536
## -5223.339501 4152.069009 5315.410645 -2051.385087 -4789.089602
## 537 538 539 540 541
## -7298.052055 -2291.044595 8140.993533 -91.544306 -8358.802767
## 542 543 544 545 546
## 1635.822498 -788.951097 178.879330 -11220.656433 -11204.523493
## 547 548 549 550 551
## 1940.123432 6884.723827 -1474.195177 681.942921 -7883.445495
## 552 553 554 555 556
## 8431.887030 730.508218 -12126.778079 9028.773409 8477.335587
## 557 558 559 560 561
## -122.022416 4632.379857 -3815.275136 13885.782131 21215.693805
## 562 563 564 565 566
## -6836.772570 -10007.880206 6501.447517 -70.273393 3166.036413
## 567 568 569 570 571
## -7672.467518 -17561.104369 6492.515216 6235.386557 1677.121754
## 572 573 574 575 576
## 2868.919603 1531.595254 -2405.801471 14490.813948 -9935.638524
## 577 578 579 580 581
## -6481.899950 8504.692602 2615.677847 -6795.839807 7292.175708
## 582 583 584 585 586
## -4046.207414 -3000.292214 15493.285287 -14774.036336 8222.392911
## 587 588 589 590 591
## -169.705318 -6447.572141 -957.658507 54.019651 -10850.495348
## 592 593 594 595 596
## 1646.543632 -7305.089478 2935.350133 8716.298878 -7691.659939
## 597 598 599 600 601
## 5692.269029 2548.232840 6657.298487 -3416.575677 5940.466255
## 602 603 604 605 606
## -8530.911228 2062.701343 1069.161418 2934.181719 1280.393685
## 607 608 609 610 611
## 179.945289 -6025.695994 7887.714216 -1403.104220 -2783.877754
## 612 613 614 615 616
## -3648.311575 -8406.176548 11815.742561 4741.841798 -9526.946894
## 617 618 619 620 621
## 11456.193818 5832.476143 -5810.031891 26152.952688 -13131.841171
## 622 623 624 625 626
## -7017.857052 2954.822167 -4369.785823 -10781.134515 11152.299713
## 627 628 629 630 631
## -21826.008663 -2519.139640 8574.081336 10994.388199 -1739.184993
## 632 633 634 635 636
## 33105.962877 -6891.516092 5453.224062 5124.154117 -2552.087519
## 637 638 639 640 641
## -5606.726608 -2170.206043 -12646.229252 -2404.688578 -2039.926484
## 642 643 644 645 646
## -2667.618454 -2997.214301 1684.323223 4290.027364 16804.880329
## 647 648 649 650 651
## 18328.314504 593.995507 4508.609538 10325.063856 19837.229817
## 652 653 654 655 656
## 376.707072 -28408.577760 -1560.597963 -2495.831797 1680.525399
## 657 658 659 660 661
## -3379.411399 -10791.042001 1536.945979 4092.514632 -1155.074757
## 662 663 664 665 666
## 12888.818947 1173.424084 1627.296009 -11877.998462 1236.440093
## 667 668 669 670 671
## 1041.176932 -5314.333488 -7539.188884 1962.290540 -3824.893055
## 672 673 674 675 676
## 2570.548345 -3493.193806 -9441.984822 -8386.320117 -3040.901539
## 677 678 679 680 681
## 108.454448 2773.084857 623.098027 -3924.265718 -1899.439974
## 682 683 684 685 686
## -1409.339764 -8335.060478 4574.622633 -2337.481472 -1491.767813
## 687 688 689 690 691
## 492.906511 10752.509338 9713.504592 10460.439642 -9850.350035
## 692 693 694 695 696
## -3704.682436 -3276.638669 5744.667318 -10527.445454 -8021.798678
## 697 698 699 700 701
## -8701.181393 -6345.473013 -4800.742631 3024.624767 -4476.163999
## 702 703 704 705 706
## -1967.718258 4150.414724 31017.680553 9374.561956 23295.360863
## 707 708 709 710 711
## 1514.309618 8170.211945 22771.485427 6401.676774 -18351.895923
## 712 713 714 715 716
## 4710.190198 -5552.666795 -197.500039 386.872312 -17356.886320
## 717 718 719 720 721
## -5334.081813 3270.340420 -3082.077875 -13043.983571 4227.189213
## 722 723 724 725 726
## -5615.517602 687.505386 -3992.490468 -12502.653178 1323.221331
## 727 728 729 730 731
## -1916.359542 -9827.748524 17226.196540 1710.546987 -2790.233425
## 732 733 734 735 736
## 5650.105960 -8702.418229 -786.024486 8075.388393 -15424.022072
## 737 738 739 740 741
## -5967.190045 7356.183858 -4846.896575 102.234528 1767.216180
## 742 743 744 745 746
## -2019.769336 -5230.930078 6354.036405 -6342.012390 22637.699451
## 747 748 749 750 751
## 7741.658245 -2038.199078 -7374.554632 23339.781623 -4389.271949
## 752 753 754 755 756
## 1307.127522 -14509.444473 56038.539541 26803.747196 14966.885573
## 757 758 759 760 761
## -10773.953328 10500.436824 7208.007908 5705.552219 -46489.628392
## 762 763 764 765 766
## -16225.992061 925.137912 -2560.656402 -3499.811297 122803.733700
## 767 768 769 770 771
## 19189.607666 43598.216161 22448.193048 11938.434145 15715.215692
## 772 773 774 775 776
## 25597.752484 -98942.631503 -6818.127541 -35886.289683 1705.958347
## 777 778 779 780 781
## -1262.683832 3361.737728 -7452.279069 -1478.587515 -1979.082167
## 782 783 784 785 786
## 3390.755658 -7191.885373 -2269.608488 3886.965686 2229.619900
## 787 788 789 790 791
## -2796.479207 -4083.180485 1705.323998 2818.306694 -88.437202
## 792 793 794 795 796
## -6740.320658 -5815.401281 -1177.415764 -1293.591274 -7848.008479
## 797 798 799 800 801
## -2381.047947 -3295.456515 -2692.436243 10697.441693 2206.440154
## 802 803 804 805 806
## 7043.204171 2882.677416 -5490.202521 8149.672822 9832.186342
## 807 808 809 810 811
## -10649.660347 -7435.529196 -7538.986867 2976.683714 4162.448298
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17246.33 20096.88 24355.91 24073.54 26429.70 23759.19 24476.36 19702.81
## 10 11 12 13 14 15 16 17
## 19439.06 16778.67 17557.06 14282.57 14333.80 14999.24 16697.83 15015.86
## 18 19 20 21 22 23 24 25
## 16051.75 15424.54 22515.39 21598.31 21077.55 22969.80 22294.99 22948.31
## 26 27 28 29 30 31 32 33
## 24796.46 18717.65 20445.85 28292.47 28350.19 28022.72 25649.31 27053.41
## 34 35 36 37 38 39 40 41
## 30901.01 31249.85 32660.70 30168.35 34142.61 37354.50 34407.11 31214.21
## 42 43 44 45 46 47 48 49
## 30060.62 20628.98 28156.63 30596.00 31686.49 38532.70 38026.69 42694.40
## 50 51 52 53 54 55 56 57
## 46935.69 39624.95 34186.96 29203.77 22340.07 28637.53 25214.11 21507.33
## 58 59 60 61 62 63 64 65
## 25920.91 27181.95 27479.31 27891.62 23757.66 40368.60 42214.75 37455.07
## 66 67 68 69 70 71 72 73
## 41667.00 46588.47 57269.28 55280.70 40506.41 38009.32 41030.80 35324.19
## 74 75 76 77 78 79 80 81
## 30771.04 21463.47 24668.74 20589.63 22678.16 17567.65 19585.47 18811.07
## 82 83 84 85 86 87 88 89
## 17837.65 15904.29 17183.58 20795.91 25218.86 26209.90 26234.89 26852.35
## 90 91 92 93 94 95 96 97
## 30982.32 29811.49 30812.02 28874.17 28073.04 28441.62 28848.68 22418.53
## 98 99 100 101 102 103 104 105
## 25437.13 18459.36 17314.14 15352.55 15652.66 16330.85 20808.78 19891.20
## 106 107 108 109 110 111 112 113
## 23406.45 23196.17 24874.27 27754.44 25249.60 21688.87 21964.62 24614.02
## 114 115 116 117 118 119 120 121
## 35491.36 33682.07 35521.72 38523.58 40481.80 38167.49 32982.39 29319.46
## 122 123 124 125 126 127 128 129
## 31404.75 29682.67 30865.62 38474.35 38116.39 37176.83 34036.66 35819.96
## 130 131 132 133 134 135 136 137
## 41224.21 40663.84 31855.17 33121.45 36314.98 32721.39 31106.76 30190.69
## 138 139 140 141 142 143 144 145
## 26743.65 28159.75 27929.11 25612.68 27639.49 26262.63 19857.22 22891.31
## 146 147 148 149 150 151 152 153
## 20715.44 23696.06 24236.69 25828.95 26011.24 27667.35 28969.63 32005.35
## 154 155 156 157 158 159 160 161
## 27470.19 26732.29 24284.67 30185.69 41691.51 40019.78 37381.63 42407.80
## 162 163 164 165 166 167 168 169
## 43818.03 47237.94 42698.54 38000.22 43431.83 59753.15 61967.88 60380.11
## 170 171 172 173 174 175 176 177
## 57202.08 55599.45 58305.20 57328.15 49611.22 52438.04 56185.05 56221.29
## 178 179 180 181 182 183 184 185
## 63358.49 53849.63 50596.76 41394.65 32924.07 36436.44 46551.76 46007.14
## 186 187 188 189 190 191 192 193
## 51966.88 57718.59 68514.22 73803.28 67490.57 67684.23 74762.83 70434.21
## 194 195 196 197 198 199 200 201
## 65951.79 55183.02 49206.16 50632.12 46221.66 38381.29 44813.56 43038.89
## 202 203 204 205 206 207 208 209
## 42676.43 43155.33 49957.31 58856.94 58486.11 60212.94 61870.67 65666.29
## 210 211 212 213 214 215 216 217
## 75143.91 67217.45 55392.67 49995.17 40980.96 37935.12 41052.43 31060.93
## 218 219 220 221 222 223 224 225
## 48054.32 55383.67 56286.63 79078.71 86582.49 88588.06 96188.30 87128.61
## 226 227 228 229 230 231 232 233
## 81120.02 80701.46 77347.51 76508.03 81250.58 82616.45 77045.40 72252.75
## 234 235 236 237 238 239 240 241
## 77913.00 64545.19 56572.37 48513.60 40115.36 44312.17 46467.42 39910.46
## 242 243 244 245 246 247 248 249
## 33582.64 43877.12 38117.29 42057.59 34312.42 33016.77 36676.33 39503.52
## 250 251 252 253 254 255 256 257
## 30322.27 36267.27 40073.13 45273.98 47995.47 47487.45 57800.43 75318.08
## 258 259 260 261 262 263 264 265
## 75132.91 68435.76 69919.32 66135.70 67583.53 61333.92 50596.01 46618.73
## 266 267 268 269 270 271 272 273
## 46828.38 42930.56 51744.98 48013.71 52165.29 50279.66 54351.78 54648.14
## 274 275 276 277 278 279 280 281
## 60672.60 58304.07 67988.34 61906.03 62116.31 60463.45 66213.04 59936.19
## 282 283 284 285 286 287 288 289
## 56496.03 46036.12 44430.99 61683.39 67219.95 67629.88 65044.46 64130.75
## 290 291 292 293 294 295 296 297
## 68136.40 72051.21 53025.52 43115.97 37120.00 47453.47 50699.09 49812.00
## 298 299 300 301 302 303 304 305
## 74036.67 79989.04 80657.48 85275.35 83471.81 78496.23 81967.13 56836.50
## 306 307 308 309 310 311 312 313
## 53093.23 52772.63 46555.51 43761.68 47369.26 39912.71 38633.30 33188.44
## 314 315 316 317 318 319 320 321
## 36977.44 36157.82 39993.79 37975.85 63792.53 61631.16 63256.66 71264.20
## 322 323 324 325 326 327 328 329
## 73654.35 99166.04 97541.80 73343.49 72139.92 70495.31 62437.54 59556.37
## 330 331 332 333 334 335 336 337
## 29469.29 33161.21 33601.25 35923.93 35263.98 41037.81 42114.69 37356.64
## 338 339 340 341 342 343 344 345
## 36570.13 36696.94 32010.36 38016.46 38680.65 38939.41 39816.25 41604.30
## 346 347 348 349 350 351 352 353
## 43428.46 43179.82 36116.09 26672.71 32023.73 30883.38 30472.95 28086.55
## 354 355 356 357 358 359 360 361
## 32771.01 36529.77 40998.70 39185.54 40447.33 42588.34 49992.42 50543.66
## 362 363 364 365 366 367 368 369
## 50745.57 53211.68 50692.10 50135.98 42772.38 39965.81 36138.20 33914.23
## 370 371 372 373 374 375 376 377
## 29967.31 37263.93 39553.62 47448.60 41413.01 40859.52 39395.07 38927.29
## 378 379 380 381 382 383 384 385
## 29784.02 34387.64 27430.98 35660.17 46005.64 49575.36 47846.66 49840.74
## 386 387 388 389 390 391 392 393
## 56068.36 65565.21 58768.36 53229.92 52958.30 60346.75 60870.76 69549.87
## 394 395 396 397 398 399 400 401
## 58642.42 60211.19 59770.77 59254.01 57739.08 56499.16 43241.79 51837.81
## 402 403 404 405 406 407 408 409
## 50837.03 49802.01 56211.04 48744.50 48054.82 46379.47 42051.47 40880.76
## 410 411 412 413 414 415 416 417
## 38942.28 33029.27 40911.62 43841.51 38507.36 33589.63 48479.37 52328.84
## 418 419 420 421 422 423 424 425
## 56262.89 48722.88 45041.71 43716.07 47306.91 35711.90 35440.78 29692.56
## 426 427 428 429 430 431 432 433
## 35289.22 43626.99 50527.55 47289.04 44354.15 41275.58 41163.63 37637.13
## 434 435 436 437 438 439 440 441
## 33770.80 31001.45 32579.47 34431.12 32433.29 37305.41 43518.54 40266.92
## 442 443 444 445 446 447 448 449
## 39972.55 42982.66 40866.14 44860.54 40101.62 31122.90 29958.94 41329.80
## 450 451 452 453 454 455 456 457
## 41010.95 46669.08 42299.85 42649.94 44272.44 47995.22 37857.03 42712.29
## 458 459 460 461 462 463 464 465
## 38129.91 45708.28 49232.65 51856.43 48581.95 50925.61 51127.39 52876.58
## 466 467 468 469 470 471 472 473
## 52373.57 55320.95 52645.94 57703.35 50954.97 48562.96 47147.36 43767.55
## 474 475 476 477 478 479 480 481
## 47528.31 55000.85 49420.93 51126.27 45908.68 44285.68 47122.12 36523.27
## 482 483 484 485 486 487 488 489
## 30076.88 31948.52 34648.52 36140.33 37107.38 30740.33 43286.02 49954.44
## 490 491 492 493 494 495 496 497
## 56793.15 51498.10 56337.11 63980.20 67796.31 54035.67 44601.91 42624.45
## 498 499 500 501 502 503 504 505
## 42948.05 43740.19 38220.11 40622.00 45930.05 51618.67 52329.21 52439.91
## 506 507 508 509 510 511 512 513
## 46134.08 47475.71 43730.32 46489.16 46154.44 39862.48 40994.96 40169.09
## 514 515 516 517 518 519 520 521
## 41276.08 43919.35 36750.67 32024.23 55943.29 64114.01 67771.44 61142.39
## 522 523 524 525 526 527 528 529
## 62483.51 76085.85 83071.12 57990.59 52854.34 49544.63 53928.97 53434.70
## 530 531 532 533 534 535 536 537
## 43606.25 48604.19 61280.20 55794.36 59196.16 63188.81 60237.80 55262.48
## 538 539 540 541 542 543 544 545
## 48716.76 47371.01 55317.83 55067.95 47618.89 49845.24 49671.69 50366.37
## 546 547 548 549 550 551 552 553
## 41003.95 32829.73 37176.85 45303.34 45100.06 46808.02 40810.54 49834.49
## 554 555 556 557 558 559 560 561
## 50991.21 40757.94 50310.52 58182.88 57547.05 61149.13 56911.22 68686.02
## 562 563 564 565 566 567 568 569
## 85394.92 75473.88 64023.55 68448.13 66570.25 67758.32 59318.10 43287.77
## 570 571 572 573 574 575 576 577
## 50304.90 56217.16 57401.37 59479.40 60127.23 57250.19 69511.64 58872.19
## 578 579 580 581 582 583 584 585
## 52587.59 60198.32 61704.13 54789.82 61063.92 56634.72 53675.71 67262.18
## 586 587 588 589 590 591 592 593
## 52673.18 60026.28 59117.57 52832.23 52136.55 52412.92 43117.60 45917.80
## 594 595 596 597 598 599 600 601
## 40537.79 44788.70 53562.52 46885.73 52751.77 55132.42 60808.29 56961.82
## 602 603 604 605 606 607 608 609
## 61781.34 53339.87 55222.12 55999.39 58310.32 58885.05 58425.27 52595.71
## 610 611 612 613 614 615 616 617
## 59665.82 57723.59 54817.31 51519.46 44473.97 55998.02 59890.09 50814.66
## 618 619 620 621 622 623 624 625
## 61229.10 65419.03 58901.05 81155.13 66260.14 58580.32 60585.64 55933.42
## 626 627 628 629 630 631 632 633
## 46257.27 56977.44 37510.57 37370.63 46950.33 57445.47 55487.75 84250.94
## 634 635 636 637 638 639 640 641
## 74425.49 76628.85 78268.09 72988.16 65698.78 62329.09 50219.69 48586.07
## 642 643 644 645 646 647 648 649
## 47476.33 45956.79 44339.53 47019.54 51642.41 66630.97 81072.29 78192.25
## 650 651 652 653 654 655 656 657
## 79097.08 84975.48 98436.01 93188.43 63423.46 60872.26 57823.05 58808.84
## 658 659 660 661 662 663 664 665
## 55245.61 45647.05 48034.20 52357.07 51548.32 63123.72 63001.28 63291.14
## 666 667 668 669 670 671 672 673
## 51732.99 53094.11 54113.76 49447.05 43419.71 46458.18 44054.17 47545.05
## 674 675 676 677 678 679 680 681
## 45294.84 38124.03 32775.76 32773.26 35525.49 40263.04 42526.12 40528.30
## 682 683 684 685 686 687 688 689
## 40551.91 41001.20 35336.95 41673.77 41170.62 41470.24 43468.06 54188.35
## 690 691 692 693 694 695 696 697
## 62655.56 70714.21 59998.54 56001.64 52880.33 58040.45 48321.94 42013.61
## 698 699 700 701 702 703 704 705
## 35902.19 32617.46 31095.66 36608.74 34870.29 35543.73 41483.61 70176.58
## 706 707 708 709 710 711 712 713
## 76342.35 93909.98 90224.93 92823.23 107865.89 106705.18 84040.67 84388.38
## 714 715 716 717 718 719 720 721
## 75716.64 72815.98 70790.17 53499.80 48892.80 52388.94 49890.84 38993.38
## 722 723 724 725 726 727 728 729
## 44567.80 40834.78 43082.49 40955.22 31651.78 35607.07 36233.03 29861.23
## 730 731 732 733 734 735 736 737
## 47949.74 50199.95 48231.61 53891.99 46289.88 46564.75 54555.31 40991.33
## 738 739 740 741 742 743 744 745
## 37399.24 45910.18 42681.05 44185.36 46957.20 46069.36 42484.39 49481.16
## 746 747 748 749 750 751 752 753
## 44496.59 65482.63 70808.91 66913.84 58840.08 78641.41 71707.87 70625.87
## 754 755 756 757 758 759 760 761
## 55846.46 104621.40 121711.11 126305.24 107810.42 110241.42 109488.02 107515.06
## 762 763 764 765 766 767 768 769
## 60139.85 45174.15 47085.51 45708.53 43682.84 152375.68 156817.50 182049.95
## 770 771 772 773 774 775 776 777
## 185620.42 179551.36 177546.53 184436.35 81539.70 72118.43 38455.76 41892.54
## 778 779 780 781 782 783 784 785
## 42301.98 46704.56 41097.16 41417.51 41259.96 45818.60 40550.04 40247.18
## 786 787 788 789 790 791 792 793
## 45366.81 48394.91 46647.47 43993.82 46735.55 50106.87 50513.18 45050.83
## 794 795 796 797 798 799 800 801
## 41082.42 41668.02 42078.58 36705.19 36787.03 36058.86 35949.42 47564.42
## 802 803 804 805 806 807 808 809
## 50296.65 56916.47 59067.35 53625.61 60795.67 68538.09 57396.24 50462.70
## 810 811
## 44308.17 48122.41
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8119
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 5.015568 0.7475272 3.826338
## t2* 2621.948107 165.2075009 867.363753
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.168075 4.931652 13.14288
## 2 lag_depvar 1593.417218 2667.019803 4398.60521Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Feb 03 00:51:21 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Feb 03 00:51:29 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Feb 03 00:51:37 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Feb 03 00:51:45 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Feb 03 00:51:53 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Feb 03 00:52:01 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Feb 03 00:52:09 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Feb 03 00:52:17 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Feb 03 00:52:25 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Feb 03 00:52:33 2025
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_25<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2025",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2020",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_25 %>%
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))| Item | 2025 | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|---|
| Agua | 0.000 | 6.993667 | 5.195333 | 5.410333 | 5.849167 | 9.93775 |
| Comida | 325.252 | 326.890000 | 366.009167 | 312.386750 | 317.896583 | 392.93367 |
| Comunicaciones | 0.000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electricidad | 55.000 | 83.582750 | 38.104750 | 47.072333 | 29.523000 | 20.60458 |
| Enceres | 0.000 | 23.989000 | 18.259750 | 24.219750 | 14.801167 | 39.01200 |
| Farmacia | 0.000 | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 14.03675 |
| Gas/Bencina | 0.000 | 44.292667 | 42.636000 | 45.575000 | 13.583667 | 17.25833 |
| Diosi | 73.999 | 33.319583 | 55.804250 | 31.180667 | 52.687833 | 37.12133 |
| donaciones/regalos | 0.000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electrodomésticos/ Mantención casa | 0.000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| VTR | 21.990 | 18.326667 | 12.829167 | 25.156667 | 19.086917 | 19.11375 |
| Netflix | 0.000 | 1.391417 | 8.713833 | 7.151583 | 7.028750 | 8.24725 |
| Otros | 0.000 | 76.164000 | 5.481667 | 5.000000 | 0.000000 | 0.00000 |
| Total | 476.241 | 614.949750 | 563.738000 | 505.988083 | 474.453167 | 558.26542 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")
tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 54 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2596, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)## Warning in bats(as.numeric(y), use.box.cox = use.box.cox, use.trend =
## use.trend, : optim() did not converge.fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2025-02-09 00:04:58 sería de: 26.545 pesos// Percentil 95% más alto proyectado: 34.743,54
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 26175.89 | 26345.07 |
| Lo.80 | 26303.19 | 26502.20 |
| Point.Forecast | 26545.35 | 26799.01 |
| Hi.80 | 31225.49 | 31922.80 |
| Hi.95 | 34028.34 | 34635.16 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(0,1,2)
##
## Coefficients:
## ma1 ma2
## -0.5604 -0.3058
## s.e. 0.1105 0.1108
##
## sigma^2 = 37825: log likelihood = -467.86
## AIC=941.72 AICc=942.09 BIC=948.47paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(0,0,1) errors
##
## Coefficients:
## ma1 intercept xreg
## 0.3568 435.9187 19.2322
## s.e. 0.1020 280.3738 8.6836
##
## sigma^2 = 35837: log likelihood = -471.56
## AIC=951.12 AICc=951.72 BIC=960.17forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 696.4208 | 723.4786 | 766.4502 |
| Lo.80 | 832.7768 | 867.6069 | 870.0364 |
| Point.Forecast | 1090.3596 | 1139.8716 | 1105.4076 |
| Hi.80 | 1347.9423 | 1431.8466 | 1423.9349 |
| Hi.95 | 1484.2984 | 1586.4089 | 1628.1543 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [4] Boom_0.9.15 scales_1.3.0 ggiraph_0.8.9
## [7] tidytext_0.4.1 DT_0.32 janitor_2.2.0
## [10] autoplotly_0.1.4 rvest_1.0.4 plotly_4.10.4
## [13] xts_0.13.2 forecast_8.21.1 wordcloud_2.6
## [16] RColorBrewer_1.1-3 SnowballC_0.7.1 tm_0.7-11
## [19] NLP_0.2-1 tsibble_1.1.4 lubridate_1.9.3
## [22] forcats_1.0.0 dplyr_1.1.4 purrr_1.0.2
## [25] tidyr_1.3.1 tibble_3.2.1 tidyverse_2.0.0
## [28] gsynth_1.2.1 lattice_0.20-45 GGally_2.2.1
## [31] ggplot2_3.5.0 gridExtra_2.3 plotrix_3.8-4
## [34] sparklyr_1.8.4 httr_1.4.7 readxl_1.4.3
## [37] zoo_1.8-12 stringr_1.5.1 stringi_1.8.3
## [40] data.table_1.15.0 reshape2_1.4.4 fUnitRoots_4021.80
## [43] plyr_1.8.9 readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] uuid_1.2-0 systemfonts_1.0.5 selectr_0.4-2
## [4] lazyeval_0.2.2 websocket_1.4.1 crosstalk_1.2.1
## [7] listenv_0.9.1 digest_0.6.34 foreach_1.5.2
## [10] htmltools_0.5.7 fansi_1.0.6 ggfortify_0.4.16
## [13] magrittr_2.0.3 doParallel_1.0.17 tzdb_0.4.0
## [16] globals_0.16.2 vroom_1.6.5 sandwich_3.1-0
## [19] askpass_1.2.0 timechange_0.3.0 anytime_0.3.9
## [22] tseries_0.10-55 colorspace_2.1-0 xfun_0.42
## [25] crayon_1.5.2 jsonlite_1.8.8 iterators_1.0.14
## [28] glue_1.7.0 gtable_0.3.4 car_3.1-2
## [31] quantmod_0.4.26 abind_1.4-5 mvtnorm_1.2-4
## [34] DBI_1.2.2 rngtools_1.5.2 Rcpp_1.0.12
## [37] lfe_2.9-0 viridisLite_0.4.2 xtable_1.8-4
## [40] bit_4.0.5 Formula_1.2-5 htmlwidgets_1.6.4
## [43] timeSeries_4032.109 gplots_3.1.3.1 ellipsis_0.3.2
## [46] spatial_7.3-14 farver_2.1.1 pkgconfig_2.0.3
## [49] nnet_7.3-16 sass_0.4.8 dbplyr_2.4.0
## [52] chromote_0.2.0 utf8_1.2.4 labeling_0.4.3
## [55] tidyselect_1.2.0 rlang_1.1.3 later_1.3.2
## [58] munsell_0.5.0 cellranger_1.1.0 tools_4.1.2
## [61] cachem_1.0.8 cli_3.6.2 generics_0.1.3
## [64] evaluate_0.23 fastmap_1.1.1 yaml_2.3.8
## [67] processx_3.8.3 knitr_1.45 bit64_4.0.5
## [70] caTools_1.18.2 future_1.33.1 nlme_3.1-153
## [73] doRNG_1.8.6 slam_0.1-50 xml2_1.3.6
## [76] tokenizers_0.3.0 compiler_4.1.2 rstudioapi_0.15.0
## [79] curl_5.2.0 bslib_0.6.1 highr_0.10
## [82] ps_1.7.6 fBasics_4032.96 Matrix_1.6-5
## [85] its.analysis_1.6.0 urca_1.3-3 vctrs_0.6.5
## [88] pillar_1.9.0 lifecycle_1.0.4 lmtest_0.9-40
## [91] jquerylib_0.1.4 bitops_1.0-7 R6_2.5.1
## [94] promises_1.2.1 KernSmooth_2.23-20 janeaustenr_1.0.0
## [97] parallelly_1.37.0 codetools_0.2-18 ggstats_0.5.1
## [100] assertthat_0.2.1 boot_1.3-28 gtools_3.9.5
## [103] MASS_7.3-54 openssl_2.1.1 withr_3.0.0
## [106] fracdiff_1.5-3 parallel_4.1.2 hms_1.1.3
## [109] quadprog_1.5-8 timeDate_4032.109 rmarkdown_2.25
## [112] snakecase_0.11.1 carData_3.0-5 TTR_0.24.4#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))